Event characteristic analysis for event input discrimination

ABSTRACT

Determining a level of congruence between modality-event characteristics is disclosed. Information can be collected from an event input source via one or more information collection modalities. Modality-event characteristics can be determined from this information. A level of congruence between the modality-event characteristics can be determined to enable initiating a response based on the level of congruence. The level of congruence can be based on satisfying a rule related to congruence between modality-event characteristics, user profile information, etc. The level of congruence can be related to a probability that the several inputs collected for an event, collected by a plurality of modalities, embody characteristics that are associated with the event occurring according to determined notions embodied in the rule and profile. Determining the level of congruence can support assertions that each input, across differing modes of capturing said input, accords with the expected inputs for an event.

TECHNICAL FIELD

The disclosed subject matter relates to an analysis of an eventcharacteristic, e.g., an analysis of an event characteristic can beemployed in discriminating between event inputs based on an incongruencerelated to the event characteristic.

BACKGROUND

By way of brief background, conventional authentication of a user inputto a device is “key and lock” type authentication, e.g., a user inputs apassword (key) that results in an unlock of a functionality (lock) wherethe password is determined to match a password on file. More advancedconventional systems can use a multifactor authentication, e.g., two ormore pieces of information can be checked (two keys are checked). Insome typical multifactor systems, the second key is often a timesensitive code to which only the user is expected to have access. Thesetechnologies, however, do not address non-user sources of the keys and,as such, are susceptible to failure. More generally, where input(s) aretreated as attributable to only one source, and therefore given ameasure of trust, this measure of trust can be leveraged to circumventtypical security measures. As an example, a first user's spoken passwordcan be recorded and then used by a second user to gain improper accesspremised on an assumption that only the first user will have the voiceassociated with the first user. This example can also be expanded tomultifactor authentication, e.g., the second user can have the firstuser's mobile device and the recorded password, where the mobile devicehas the second key and the first key is spoofed with the recorded voice,the security measures are again defeated.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is an illustration of an example system that facilitatesdetermining a congruence of event characteristics captured for an eventvia different modalities in accordance with aspects of the subjectdisclosure.

FIG. 2 is a depiction of an example system that facilitates determininga congruence of 1 to N event characteristics captured for an eventsource via 1 to N different modalities in accordance with aspects of thesubject disclosure.

FIG. 3 illustrates an example system that facilitates determining acongruence of event characteristics captured for an event source viadifferent modalities and via a plurality of user equipments inaccordance with aspects of the subject disclosure.

FIG. 4 illustrates an example system that facilitates determining acongruence of event characteristics captured for an event via differentmodalities employing a remotely located analysis component in accordancewith aspects of the subject disclosure.

FIG. 5 illustrates an example of depicting determining a congruence ofevent characteristics captured for an event via different modalities inaccordance with aspects of the subject disclosure.

FIG. 6 illustrates an example method facilitating initiating a responsebased on determining a congruence of event characteristics captured foran event via different modalities in accordance with aspects of thesubject disclosure.

FIG. 7 depicts an example method facilitating determining a congruenceof 1 to (N+1) event characteristics captured for an event source via 1to (N+1) different modalities from a plurality of user equipments inaccordance with aspects of the subject disclosure.

FIG. 8 illustrates an example method facilitating determining acongruence of event characteristics captured for an event via differentmodalities based on remotely stored rules in accordance with aspects ofthe subject disclosure.

FIG. 9 depicts an example schematic block diagram of a computingenvironment with which the disclosed subject matter can interact.

FIG. 10 illustrates an example block diagram of a computing systemoperable to execute the disclosed systems and methods in accordance withan embodiment.

DETAILED DESCRIPTION

The subject disclosure is now described with reference to the drawings,wherein like reference numerals are used to refer to like elementsthroughout. In the following description, for purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of the subject disclosure. It may be evident, however,that the subject disclosure may be practiced without these specificdetails. In other instances, well-known structures and devices are shownin block diagram form in order to facilitate describing the subjectdisclosure.

Conventional authentication of a user input to a device, as previouslystated, is typically of a “key and lock” type. Keys, such as, userpasswords, vocal patterns, fingerprints, iris scans, etc., can result inan unlock of a ‘lock’, e.g., a functionality, action, alert, system,etc., where the key is determined to match a record on file. Moreadvanced conventional systems can use a multifactor authentication,e.g., two or more pieces of information can be checked (two keys arechecked against stored values). These technologies, however, do notaddress the validity of the source of the key itself, e.g., is thepassword coming from the actual user associated with the password or,conversely, is the password being presented artificially, and thus theseconventional systems can be susceptible to other types of failures,e.g., spoofing, etc., even where the password itself is correct. Moregenerally, where input(s) are de facto treated as valid where correctrather than valid where correct and validated to a source, they can beleveraged to circumvent typical security measures. As an example, a trueuser's passcode can be recorded such as by a key logger, etc., and thenused by a malicious user to gain access by sending the codeelectronically rather than physically entering it into a device via akeyboard, touchpad, etc. Where the passcode can be checked against othercharacteristics of the passcode entry event, e.g., is an actual touchdetected on a keypad, touch screen, etc., is a face visible via anonboard camera, is the device in an orientation associated with normalpasscode entry (e.g., not face down), can the true user's voice bedetected in the background, etc., the validity of the passcode entry canbe predicated on a congruence of these additional event characteristicsand can improve the security associated with use of the examplepasscode.

The present disclosure addresses analysis of event characteristics viaadditional modalities to determine a level of congruence that canfacilitate validation of another characteristic of the event. In anaspect, information related to an event can be captured by a pluralityof modalities. Characteristics of the event can be extracted from thisinformation, e.g., modality-event characteristics. As an example, entryof text into an email can be associated with physical entry of the textvia an input device, movement of the input device associated with thephysical entry of the text (finger tapping can cause a device tojiggle), with the device being oriented so as to provide access to theinput device (face up), the occasional sound of the user's nativelanguage (e.g., office conversation, the user speaking to someone,etc.), an orientation of the user and the device (the user can be infront of and facing the device to enter text) that can be captured by animaging device, etc. The characteristics of these other aspects of theevent can be analyzed to indicate a level of congruence. As an example,where the device is face down, a detected language is foreign, and thedevice is motionless, etc., it can be determined that the entry of textis not likely due to the user entering the text because there is a lowlevel of congruence between the modality-event characteristics. As afurther example, where the device is face up, a background voiceattributable to the user's wife and child are detected, the devicejiggles in correlation to text entry and is still in periods where textisn't being entered, and a front facing camera captures the user's face,there can be a high level of congruence and the text entry can betreated as likely attributable to the user.

The present disclosure is not limited to determining congruencies forvalidating text entry and can be applied to nearly any form of inputevent. As an example, speaking into a corded or cordless microphoneattached to a phone can be spoofed by using an antenna to inducecurrents in a microphone cord or can mimic the data sent from a wirelessmicrophone and received by the phone. This type of event can be analyzedto check for electromagnetic radiation (EM) that has a sufficient levelof correlation to the audio signal being received by the phone, e.g., ifEM goes up when a spoken word is received and the EM radiation goes downwhen there is a pause, then there can be a lower level of congruencethat can indicate that the audio information may not be from a trueuser. A further modality-event characteristic can be associated with thephone camera capturing the true user's face and mouth movements, e.g.,where the mouth isn't moving in time to the audio information it can beless likely that the true user is generating the audio information, orwhere the another user's face (not the true user) is detected asprominently before the phone, e.g., by a front facing camera, then it isless likely to be congruent with the audio information and confidence inthe source can be decreased. Nearly any event input can be associatedwith multiple modality-event characteristics that can be analyzed todetermine a level of congruence and all such event inputs are to beconsidered within the scope of the present disclosure even where notexpressly disclosed herein for the sake of clarity and brevity.

Rules and profiles can also be employed in conjunction with the analysisof the modality-event characteristics. Rules can relate to broadlyapplicable conventions, for example, higher security inputs can beaccorded higher levels of required congruence, e.g., a password entrycan be invalidated without relatively higher levels of congruence ascompared to validating a low security entry, e.g., text entry into acalendar application etc., which can be allowed with comparatively lowlevels of congruence. Profiles can be employed to personalize congruencedeterminations. As an example, a profile can require that all forms ofvalid event inputs be determined to have high congruence, lowcongruence, perfect congruence, etc., that one modality is given moreweight than another modality, e.g., facial recognition congruence withan event input be more strongly weighted than an infrared/thermalimaging modality, etc., that a modality-event characteristic be ignored,e.g., that EM radiation modalities not be employed in congruencedetermination, etc., or nearly any other personalization. Moreover,profiles can comprise characteristics that can be associated withindividual true users, for example, images of the user, images of familymembers, the sound/voices of a true user's office, user schedules, inputdevice types/models/brands/identities, etc. As such, where a pluralityof modality-event characteristics are checked, there can beindividualization of the congruence determination based on the userprofile as well as alteration of the general rules that are applied tothe congruence determination via rules.

In some embodiments, congruence determinations can be performed in adevice receiving input. In some instances, the device receiving inputcan gather modality-event characteristic data from other device. Inother instances, the input device can be enabled with other sensingequipment to allow collection of modality-event characteristics by thedevice itself. Modern smartphones, for example, are connected devicesthat embody numerous sensors, e.g., microphones, camera, tilt sensors,motion sensors, GPS, pressure sensors, touch sensors, fingerprintsensors, etc., and can also be connected to other devices, e.g., via awired or wireless connection, such as IR sensors, remote cameras,proximity sensors, beacons, etc., that can all source modality-eventcharacteristics to the smartphone contemporaneous with event input atthe smartphone. Further, processing of the analysis can occur on thedevice receiving the event input and/or on other devices located locallyor remotely from the event input receiving device. As an example,processing of the analysis can occur in the cloud, e.g., on a remotelylocated server connected to a device via at least a wired or wirelesslink, on a local device connected to an event input receiving device,e.g., on a desktop computer validating event input on a proximate tabletcomputer, or on the event input receiving device itself, e.g., on asmartphone receiving event input on the smartphone itself. Similarly,rules and/or profile information can be stored locally and/or remotely.

To the accomplishment of the foregoing and related ends, the disclosedsubject matter, then, comprises one or more of the features hereinaftermore fully described. The following description and the annexed drawingsset forth in detail certain illustrative aspects of the subject matter.However, these aspects are indicative of but a few of the various waysin which the principles of the subject matter can be employed. Otheraspects, advantages and novel features of the disclosed subject matterwill become apparent from the following detailed description whenconsidered in conjunction with the provided drawings.

FIG. 1 is an illustration of a system 100, which facilitates determininga congruence of event characteristics captured for an event viadifferent modalities in accordance with aspects of the subjectdisclosure. System 100 can comprise modality-event characteristic (MEC)analysis component (MECAC) 120. MECAC 120 can receive MEC data 130 andcan determine MEC congruence information (MECCI) 190. MECAC 120 canreceive MEC data 130 from one or more sources. MECAC 120 can, in anaspect, extract data related to a characteristic of an event inputreceived via a modality and can analyze the significance of thecharacteristic in relation to other characteristics from other eventinput received by other modalities that are contemporaneous with theevent. As an example, where an event input is a video signal capturedwith a first video modality, the characteristic can be a binary stateassociated with the mouth of a user. This can be analyzed in relation toanother event input, for example, an audio event input captured by amicrophone modality, wherein the characteristic under analysis is theperiodic nature of loud and quiet periods in the audio data. This can beanalyzed against a third event input captured by an accelerometermodality, wherein the characteristic is a time variant level of jiggle.These three characteristics can be associated with the same input deviceand can be contemporaneous. The analysis can then determine a level ofcongruence between the modality-event characteristics, e.g., howcongruous is the mouth moving and sound level, how congruous is themouth moving and the jitter, how congruous is the sound level and thejiggle, how congruous is the mouth moving, sound level, and jiggle, etc.From this example, where the true user is typing input, the jiggle andsound (tapping sound of a keyboard for example) can be congruous butneither can be congruous with the mouth movement as the true user isperhaps not speaking while typing (although he could be mouthing thewords she is typing, which could cause the mouth movement to also becongruous). In another aspect of this example, where the user is doingspeech-to-text input, the mouth movement and sound level can becongruous while the jiggle characteristic is not, perhaps the device issitting on a table while the true user is dictating an email, forexample. Of note, only three modality-event characteristics areillustrated in this example, but the disclosure contemplates nearly anynumber of MECs can be analyzed to determine congruence between one ormore MECs.

MEC data 130 can comprise data related to a MEC. As disclosed herein, aMEC can be a characteristic associated with an event and modality. Assuch, a MEC can typically be extracted from nearly any informationsource contemporaneously associated with an event. Modalities caninclude video, images, audio, EM, motion, tilt, proximity, orientation,direction, pressure, temperature, capacitance, resistance, chemicalcomposition, etc., or even information itself, e.g., brand, model, make,manufacturer, source identification, encryption type, identifiedlanguage or dialect, etc. Moreover, different characteristics can becaptured for any given modality, for an example image, color saturation,facial recognition, fingerprint, iris pattern, skyline pattern, logo(s),weather, etc., for an example audio input, volume, frequency, spokenlanguage/dialect, sound pressure, the sound of a train in thebackground, the sound of a planes in the background, etc. It will benoted that numerous other examples are readily appreciated though theycannot all be explicitly state herein for the sake of brevity andclarity.

The analysis of MEC data 130 by MECAC 120 can result in MECCI 190. MECCI190 can comprise information associated with congruence between MECs. Assuch, MECCI 190 can comprise congruence information for some, none, orall MECs associated with MEC data 130. In an aspect, MECCI 190 cancomprise information that relates to a level of congruence between MECsthat can enable a response to be initiated. As such, where there is ahigh level of congruence between the MECs associated with an event, ‘noaction’ can be an appropriate response, however, where congruence dropsor was low, an alert can be initiated, a lockout can be initiated,tracking can be initiated, etc. Of note, congruence can change, such asover time, for an event. As an example, where a true user logs into anaccount with high MEC congruence, no action may be initiated, however,where the true user puts down the device without logging out and anotheruser begins entry, the congruence can drop, for example the face may notbe recognized, etc., which can cause an alarm to be initiated, for dataassociated with the drop in congruence to be quarantined, etc. Where,for example, a true user logs into a bank account on his mobile devicebut does not log out and then his mobile is stolen, a criminal trying totake money out of the bank account, because the true user is stilllogged in, can have his intentions frustrated where the congruence levelhas dropped. Other examples will be readily appreciated but are notexplicitly recited for brevity, although all such examples are withinthe scope of the instant disclosure.

FIG. 2 is a depiction of a system 200 that can facilitate determining acongruence of 1 to N event characteristics captured for an event sourcevia 1 to N different modalities in accordance with aspects of thesubject disclosure. System 200 can comprise MECAC 220. MECAC 220 canreceive MEC data 230 and can determine MECCI 290. MECAC 220 can receiveMEC data 230 from one or more sources.

System 200 can comprise event input source 202. Event input source canbe proximate to user equipment (UE) 240. UE 240 can receive an eventinput from event input source 202, e.g., event input via 1^(st) mode 210to event input via N^(th) mode 218. Event input source 202 can bedetectable or observable by UE 240, wherein the detection andobservation ca be by way of event input via 1^(st) mode 210 to eventinput via N^(th) mode 218. In an aspect, event input source 202 can beany event that allows for detection or observation of, e.g., receivingof, event input via 1^(st) mode 210 to event input via N^(th) mode 218.As an example, event input source 202 can be a user entering a password,whereby event input via 1^(st) mode 210 to event input via N^(th) mode218 can comprise the password entry, an image of the user entering thepassword, audio captured from the area proximate to the user enteringthe password, EM radiation from the area proximate to the user enteringthe password, facial recognition information, motion information of theUE or of the area proximate to the user entering the password, etc. Asanother example, event input source 202 can be a user using near fieldcommunication (NFC) enabled credit card to pay for items or services,whereby input via 1^(st) mode 210 to event input via N^(th) mode 218 cancomprise NFC information, an image of the credit card user, audiocaptured from the area proximate to the user, EM radiation from the areaproximate to the user, facial recognition information, motioninformation of the UE or of the area proximate to the user, or nearlyany other information related to the event determined contribute to thescene, e.g., the event input source 202.

UE 240 can, in an aspect, simply pass input via event input via 1^(st)mode 210 to event input via N^(th) mode 218 as MEC data 230 to MECAC220. In another aspect, UE 240 can extract or determine MEC data 230from input from event input via 1^(st) mode 210 to event input viaN^(th) mode 218 before enabling access to MEC data 230 by MECAC 220. Insome embodiments, UE 240 can comprise MECAC 220. In other instances,MECAC 220 can be discrete and separate from UE 240 and located local to,or remote from, UE 240. Event input via 1^(st) mode 210 to event inputvia N^(th) mode 218 can comprise information about event input source202 that can enable extraction or determination of characteristics ofevent input source 202. As such, MEC data 230 can comprise informationabout the characteristics of event input source 202 with regard to themodality that is associated with the capture of the information leadingto the characteristic. As an example, where the 1^(st) mode is an imageof a user, event input via 1^(st) mode 210 can comprise informationabout the image that can facilitate extraction of characteristics aboutthe image, e.g., facilitating facial recognition, iris patterndetection, hair color, eye color, physiological aspects of the user thatcan be derived from the image such as flared nostrils, flushed cheeks,bloodied lip, wearing makeup, the presence of glasses/contact lenses,etc. These characteristics can be embodied in MEC data 230 to facilitateMECAC 220 in determining a congruence of the characteristics across aplurality of modalities, e.g., via event input via 1^(st) mode 210 toevent input via N^(th) mode 218 presented as MEC 230 to MECAC 220.

In an aspect, MECAC 220 can extract data related to a characteristic ofan event input received via a modality and can analyze the significanceof the characteristic in relation to other characteristics from otherevent input received by other modalities that are contemporaneous withthe event, e.g., event input via 1^(st) mode 210 to event input viaN^(th) mode 218. As an example, where an event input is a video signalcaptured with a first video modality, the characteristic be a stateassociated with the eye movement of a user. This can be analyzed inrelation to another event input, for example, an audio event inputcaptured by a microphone modality, wherein the characteristic can be adetermined level of tension in the voice based on vocal analysis. Thiscan be analyzed against other event inputs from event input source 202.These three characteristics can be associated with the same inputdevice, e.g., UE 240, and can be contemporaneous. The analysis can thendetermine a level of congruence between the modality-eventcharacteristics, e.g., how congruous is the eye movement and vocalstress level, how congruous is the eye movement and the othercharacteristics, etc. From this example, where the true user is displaysrapid eye movement and has a high level of vocal stress, it can bedetermined that the user is acting under duress, that the user isviolating a social norm, etc.

MEC data 230 can comprise data related to a MEC. As disclosed herein, aMEC can be a characteristic associated with an event and modality. Assuch, a MEC can typically be extracted from nearly any informationsource contemporaneously associated with an event, e.g., event input via1^(st) mode 210 to event input via N^(th) mode 218. Modalities caninclude video, images, audio, EM, motion, tilt, proximity, orientation,direction, pressure, temperature, capacitance, resistance, chemicalcomposition, etc., or even information itself, e.g., brand, model, make,manufacturer, source identification, encryption type, identifiedlanguage or dialect, etc. Moreover, different characteristics can becaptured for any given modality, for an example image, color saturation,facial recognition, fingerprint, iris pattern, skyline pattern, logo(s),weather, etc., for an example audio input, volume, frequency, spokenlanguage/dialect, sound pressure, the sound of a train in thebackground, the sound of a planes in the background, etc. It will benoted that numerous other examples are readily appreciated though theycannot all be explicitly state herein for the sake of brevity andclarity.

The analysis of MEC data 230 by MECAC 220 can result in MECCI 290. MECCI290 can comprise information associated with congruence between MECs. Assuch, MECCI 290 can comprise congruence information for some, none, orall MECs associated with MEC data 230. In an aspect, MECCI 290 cancomprise information that relates to a level of congruence between MECsthat can enable a response to be initiated. As such, where there is alow level of congruence between the MECs associated with an event, anaction can be an appropriate response. Where, for example, MECCI 290indicates a high level of congruence for rapid eye movement and highvocal stress for a customer in line at a bank, an alert can be sent tobank security regarding the ‘nervous’ customer. As another example,where MECCI 290 indicates a high level of congruence for rapid eyemovement and high vocal stress while a user is online shopping via amobile device, e.g., UE 240, a customer service session can be initiatedvia the mobile device to aid the anxious user with their purchase. Otherexamples will be readily appreciated but are not explicitly recited forbrevity, although all such examples are within the scope of the instantdisclosure. Of note, congruence can change, such as over time, for anevent.

FIG. 3 illustrates a system 300 that facilitates determining acongruence of event characteristics captured for an event source viadifferent modalities and via a plurality of user equipments inaccordance with aspects of the subject disclosure. System 300 cancomprise MECAC 320. MECAC 320 can receive MEC data 330 and can determineMECCI 390. MECAC 320 can receive MEC data 330 from one or more sources,e.g., UE 340, UE 342, etc.

System 300 can comprise event input source 302. Event input source canbe proximate to UE 340, 342, etc. UE 340 can receive an event input fromevent input source 302, e.g., event input via 1^(st) mode 310 to eventinput via N^(th) mode 318. UE 342 can receive an event input from eventinput source 302, e.g., event input via (N+1)^(th) mode 319. Event inputsource 302 can be detectable or observable by UE 340, 342, etc., whereinthe detection and observation can be by way of event input via 1^(st)mode 310 to event input via (N+1)^(th) mode 319. In an aspect, eventinput source 302 can be any event that allows for detection orobservation of, e.g., receiving of, event input via 1^(st) mode 310 toevent input via (N+1)^(th) mode 319. As an example, event input source302 can be a driver asking for directions via a vehicle navigationsystem, whereby event input via 1^(st) mode 310 to event input via(N+1)^(th) mode 319 can comprise vocalization of an address, an image ofthe driver, an image of surrounding traffic, an image of any passengers,audio captured from the area proximate to the driver, facial recognitioninformation, motion information of the UE, e.g., UE 340, 342, thevehicle, etc.

UE 340, 342, etc., can, in an aspect, provide input via event input via1st mode 310 to event input via (N+1)^(th) mode 319 in an unchanged formas MEC data 330/332 to MECAC 320. In another aspect, UE 340, 342, etc.,can extract or determine MEC data 330 from input from event input via1^(st) mode 310 to event input via (N+1)^(th) mode 319 before enablingaccess to MEC data 330/332 by MECAC 320. In some embodiments, UE 340,342, etc., can comprise MECAC 320. In other instances, MECAC 320 can bediscrete and separate from UE 340, 342, etc., and located local to, orremote therefrom. Event input via 1^(st) mode 310 to event input via(N+1)^(th) mode 319 can comprise information about event input source302 that can enable extraction or determination of characteristics ofevent input source 302. As such, MEC data 330/332 can compriseinformation about the characteristics of event input source 302 withregard to the modality that is associated with the capture of theinformation leading to the characteristic. These characteristics can beembodied in MEC data 330/332 to facilitate MECAC 320 in determining acongruence of the characteristics across a plurality of modalities,e.g., via event input via 1^(st) mode 310 to event input via (N+1)^(th)mode 319 presented as MEC 330/332 to MECAC 320.

In an aspect, MECAC 320 can extract data related to a characteristic ofan event input received via a modality and can analyze the significanceof the characteristic in relation to other characteristics from otherevent input received by other modalities that are contemporaneous withthe event, e.g., event input via 1^(st) mode 310 to event input via(N+1)^(th) mode 319. This can be analyzed against other event inputsfrom event input source 302. These characteristics can be associatedwith the same event input source 302 via input device(s), e.g., UE 340,342, etc., and can be contemporaneous. The analysis can then determine alevel of congruence between the modality-event characteristics.

In an aspect, the inclusion of UE 342 can present several pathways foraccess to event input via (N+1)^(th) mode 319. In a first embodiment,event input via (N+1)^(th) mode 319 can be simultaneously captured by UE340 and UE 342. In another embodiment, event input via (N+1)^(th) mode319 can be captured by UE 342 and passed to UE 340 via path 319A-C.Whereby UE 340 can then pass event input via (N+1)^(th) mode 319, viapath 319C, to MECAC 320 as MEC data 330. Furthermore, UE 342 can receiveevent input via (N+1)^(th) mode 319 and enable access to correspondingMEC data 332. MEC data 332 can be accessed by MECAC 320 via path 332Aand/or from UE 340 via path 332B as part of MEC data 330, e.g., MEC data332 can be incorporated into MEC data 330 when received by UE 340 fromUE 342 via path 332B.

MEC data 330, 332, etc., can comprise data related to a MEC. Asdisclosed herein, a MEC can be a characteristic associated with an eventand modality. As such, a MEC can typically be extracted from nearly anyinformation source contemporaneously associated with an event, e.g.,event input via 1^(st) mode 310 to event input via (N+1)^(th) mode 319.Modalities can include video, images, audio, EM, motion, tilt,proximity, orientation, direction, pressure, temperature, capacitance,resistance, chemical composition, etc., or even information itself,e.g., brand, model, make, manufacturer, source identification,encryption type, identified language or dialect, etc. Moreover,different characteristics can be captured for any given modality, for anexample image, color saturation, facial recognition, fingerprint, irispattern, skyline pattern, logo(s), weather, etc., for an example audioinput, volume, frequency, spoken language/dialect, sound pressure, thesound of a train in the background, the sound of a planes in thebackground, etc. It will be noted that numerous other examples arereadily appreciated though they cannot all be explicitly stated hereinfor the sake of brevity and clarity.

The analysis of MEC data 330, 332, etc., by MECAC 320 can result inMECCI 390. MECCI 390 can comprise information associated with congruencebetween MECs. As such, MECCI 390 can comprise congruence information forsome, none, or all MECs associated with MEC data 330, 332, etc. In anaspect, MECCI 390 can comprise information that relates to a level ofcongruence between MECs that can enable a response to be initiated. Assuch, the level of congruence between the MECs associated with an eventcan be associated with a response determined to be appropriate. Of note,congruence can change, such as over time, for an event.

MECAC 320 can, as illustrated, comprise characteristic extractioncomponent 350. Characteristic extraction component 350 can extract avalue ascribed to characteristic based on MEC data 330, 332, etc. As anexample, MEC data 330 can comprise information related to an audiomodality associated with event input source 302. Characteristicextraction component 350, in this example, can extract certaintime-frequency relationships, amplitude-time relationships, Fourier orother transform information, etc., from the information related to theaudio input to enable this characteristic to be analyzed for congruencewith other characteristics. Further, MECAC 320 can comprisecharacteristic correlation component 360. Characteristic correlationcomponent 360 can determine a correlation between characteristics, e.g.,characteristics extracted from MEC data 330, 332, etc., viacharacteristic extraction component 350. In an aspect this can allow forexclusion of uncorrelated characteristics in determining a level ofcongruence between characteristics.

In an aspect, MECAC 320 can also comprise correlation rule component 370and profile component 380. Rules and profiles can also be employed inconjunction with the analysis of the modality-event characteristics.Correlation rule component 370 can facilitate access to one or morerule, wherein a rule relates to broadly applicable conventions relatedto determining a level of congruence. As an example, a rule can relateto determining similarity between waveforms, applying threshold values,defining normal rates of change in MEC values, etc. In an aspect, a rulecan be stored by correlation rule component 370 or can be received bycorrelation rule component 370, such as from a local or remotely locateddata store.

Profiles, in comparison to rules, can be employed to personalizecongruence determinations. Profile component 380 can enable access to aprofile value comprised in one or more profiles. In an aspect, a profilecan be stored by profile component 380 or can be received by profilecomponent 380, such as from a local or remotely located data store. Aprofile value, for example, can indicate weighting of modalities wherebysome modalities can have a greater impact on MECCI 390 than othermodalities, can indicate modalities that are to be ignored, modalitiesthat are to always be used, etc., can designate different rankings ofmodalities for different UEs, etc. As an example, a profile can indicatethat a video modality is to always be employed in determining the levelof congruence but that the video source should be selected from the UE,e.g., selecting between UE 340, 342, etc., that has the highestresolution video stream. Moreover, profiles can comprise characteristicsthat can be associated with individual true users, for example, imagesof the user, images of family members, the sound/voices of a true user'soffice, user schedules, input device types/models/brands/identities,etc. As such, where a plurality of modality-event characteristics arechecked, there can be individualization of the congruence determinationbased on the user profile, via profile component 380, as well asapplication of updateable general rules, via correlation rule component370, that can be applied to the congruence determination.

FIG. 4 illustrates a system 400 that facilitates determining acongruence of event characteristics captured for an event via differentmodalities employing a remotely located analysis component in accordancewith aspects of the subject disclosure. System 400 can comprise MECAC420. MECAC 420 can receive MEC data 430 and can determine MECCI 490.MECAC 420 can receive MEC data 430 from one or more sources related toan event input source 402. Event input source can be proximate to userequipment (UE) 440. UE 440 can receive an event input from the eventinput source, e.g., event input via 1^(st) mode 410 to event input viaN^(th) mode 418. An event input source can be detectable or observableby UE 440, wherein the detection and observation ca be by way of eventinput via 1^(st) mode 410 to event input via N^(th) mode 418.

UE 440 can, in an aspect, simply pass input via event input via 1^(st)mode 410 to event input via N^(th) mode 418 to MECAC 420 as MEC data430. In another aspect, UE 440 can extract or determine MEC data 430from input from event input via 1st mode 410 to event input via N^(th)mode 418 before enabling access to MEC data 430 by MECAC 420. Eventinput via 1^(st) mode 410 to event input via N^(th) mode 418 cancomprise information about an event input source that can enableextraction or determination of characteristics of the event inputsource. As such, MEC data 430 can comprise information about thecharacteristics of the event input source via the modality that isassociated with the capture of the information. These characteristicscan be embodied in MEC data 430 to facilitate MECAC 420 in determining acongruence of the characteristics across a plurality of modalities,e.g., via event input via 1^(st) mode 410 to event input via N^(th) mode418. Characteristics can be analyzed in relation to anothercharacteristic associated with another modality for capturinginformation related to the event input. The analysis can then determinea level of congruence between the modality-event characteristics.

MEC data 430 can comprise data related to a MEC. As disclosed herein, aMEC can be a characteristic associated with an event and modality. Assuch, a MEC can typically be extracted from nearly any informationsource contemporaneously associated with an event, e.g., event input via1^(st) mode 410 to event input via N^(th) mode 418. Modalities caninclude video, images, audio, EM, motion, tilt, proximity, orientation,direction, pressure, temperature, capacitance, resistance, chemicalcomposition, etc., or even information itself, e.g., brand, model, make,manufacturer, source identification, encryption type, identifiedlanguage or dialect, etc. Moreover, different characteristics can becaptured for any given modality, for an example image, color saturation,facial recognition, fingerprint, iris pattern, skyline pattern, logo(s),weather, etc., for an example audio input, volume, frequency, spokenlanguage/dialect, sound pressure, the sound of a train in thebackground, the sound of a planes in the background, etc. It will benoted that numerous other examples are readily appreciated though theycannot all be explicitly state herein for the sake of brevity andclarity.

The analysis of MEC data 430 by MECAC 420 can result in MECCI 490. MECCI490 can comprise information associated with congruence between MECs. Assuch, MECCI 490 can comprise congruence information for some, none, orall MECs associated with MEC data 430. In an aspect, MECCI 490 cancomprise information that relates to a level of congruence between MECsthat can enable a response to be initiated. Of note, congruence canchange, such as over time, for an event.

MECAC 420 can be communicatively coupled to a security component of UE440. As such, a response can be triggered by MECAC 420 based on a levelof congruence between MECs. This can be in addition to facilitatingaccess to MECCI 490 via MECAC 420. As an example, a response can causesecurity component 446 to reject input associated with an event input toUE 440, terminate or suspend access to data or resources of UE 440,request additional validation, cause an alarm, etc. In another aspect,security component 446 can communicate information, via input/output(I/O) component 444 of UE 440 to external devices or systems (noillustrated), e.g., via communication framework 492.

UE 440 can comprise I/O component 444 that can facilitate communicationbetween MECAC 420 and other devices or systems, both internal andexternal to UE 440. In an aspect, MECCI 490 can be stored at data store448, 494, etc., shared with MECAC 422, etc., or with another local orremotely located device, such as a remote server, etc., viacommunications framework 492 by way of I/O component 444. In a furtheraspect, rule and/or profile information can be received at MECAC 420 viaI/O component 444, e.g., from data store 448, 494, etc., or from otherlocal or remote devices, etc., via communication framework 492.

FIG. 5 is a diagram 500 that depicts determining a congruence of eventcharacteristics captured for an event via different modalities inaccordance with aspects of the subject disclosure. Diagram 500illustrates UE 540 with a wired headphone/microphone attachment. Ofnote, it has been shown that EM radiation can be used to spoof vocalinput to a microphone in this arrangement, e.g., an EM signal can beused to imitate user voice commands by inducing currents in themicrophone wires for the headset attachment. At 502A, a true user canvocalize into the microphone of the headset attached to 540, e.g., via510A. UE 540 can comprise a MECAC, e.g., MECAC 520, etc. In the case ofthe true user vocalizing, MEC data 530A can comprise characteristicinformation 530A1 for a microphone modality, e.g., frequency oramplitude over time, etc., and EM characteristic 530A2, e.g., relatingan amount of EM radiation received by UE 540 contemporaneously with themicrophone characteristic. Where the true user is vocalizing, EMradiation can be low and substantially different in character from themicrophone characteristic. This can be associated with a level ofcongruence indicating that it is unlikely that EM radiation caused thedetected microphone characteristic, e.g., that the microphone reflects atrue vocal signal received by the microphone. In contrast, where antenna502B produces EM radiation that induces a microphone-like currentdetected by UE 540, e.g., at 510B, this can be associated with MEC 530B.MEC 530B can comprise a microphone characteristic and EM characteristicsimilar to 530A, however, in the case where antenna 502B is sending theEM radiation, EM characteristic 530B2 can be substantially non-zero. Insome instances, EM characteristic 530B2 can be determined to be similarto microphone characteristic 530B1. This can lead to a differentdetermined level of congruence between 530B1 and 530B2 than wasdetermined for 530A1 and 530A2. In response to the similarity between530B1 and 530B2, it can be determined that 502B can be attempting tospoof a vocal input such as 502A. As such, MECCI data, e.g., 590, etc.,can be employed to initiate a response to the determined level ofcongruence, e.g., in the ‘A case’ the level of congruence can result inallowing the vocal input, and in the ‘B case’ the different level ofcongruence can result in disallowing the possibly spoofed vocal input.

In some embodiments, UE 540 can comprise MECAC 520. In otherembodiments, MECAC 520 can be located locally or remotely from UE 540.MECAC 520 can determine MECCI 590 based on MECs, such as those containedin 530A, 530B, etc., e.g., 530A1, 530A2, 530B1, 530B2. Moreover, MECCI590 can be further based on a rule and/or a profile, as disclosedherein. In an embodiment, MECCI 590 can be determined from a formulasuch as: f(530 x 1, 530 x 2, . . . , 530 xN, rule, profile), where thenotation 530 x 1 can be 530A1 in the ‘A case’ and 530B1 in the ‘B case’.Further, MECCI 590, a rule, or profile data of a profile can be storedand communicated to/from data store 548. In some embodiments, data store548 can be comprised in UE 540. In other embodiments, data store 548 canbe located local to, or remote from, UE 540.

In view of the example system(s) described above, example method(s) thatcan be implemented in accordance with the disclosed subject matter canbe better appreciated with reference to flowcharts in FIG. 6-FIG. 8. Forpurposes of simplicity of explanation, example methods disclosed hereinare presented and described as a series of acts; however, it is to beunderstood and appreciated that the claimed subject matter is notlimited by the order of acts, as some acts may occur in different ordersand/or concurrently with other acts from that shown and describedherein. For example, one or more example methods disclosed herein couldalternatively be represented as a series of interrelated states orevents, such as in a state diagram. Moreover, interaction diagram(s) mayrepresent methods in accordance with the disclosed subject matter whendisparate entities enact disparate portions of the methods. Furthermore,not all illustrated acts may be required to implement a describedexample method in accordance with the subject specification. Furtheryet, two or more of the disclosed example methods can be implemented incombination with each other, to accomplish one or more aspects hereindescribed. It should be further appreciated that the example methodsdisclosed throughout the subject specification are capable of beingstored on an article of manufacture (e.g., a computer-readable medium)to allow transporting and transferring such methods to computers forexecution, and thus implementation, by a processor or for storage in amemory.

FIG. 6 illustrates a method 600 that facilitates initiating a responsebased on determining a congruence of event characteristics captured foran event via different modalities in accordance with aspects of thesubject disclosure. At 610, method 600 can comprise receivingmodality-event characteristic (MEC) data. MEC data can comprise datarelated to a MEC. As disclosed herein, a MEC can be a characteristicassociated with an event and modality. As such, a MEC can be extractedfrom nearly any information source contemporaneously associated with anevent via a given modality. Modalities can include video, images, audio,EM, motion, tilt, proximity, orientation, direction, pressure,temperature, capacitance, resistance, chemical composition, etc., oreven information itself, e.g., brand, model, make, manufacturer, sourceidentification, encryption type, identified language or dialect, etc.Moreover, different characteristics can be captured for any givenmodality, for an example image, color saturation, facial recognition,fingerprint, iris pattern, skyline pattern, logo(s), weather, etc., foran example audio input, volume, frequency, spoken language/dialect,sound pressure, the sound of a people in the background, the sound of atraffic in the background, etc. It will be noted that numerous otherexamples can be readily raised though they cannot all be explicitlystated herein for the sake of brevity and clarity.

MEC data can be related to an event input source that can be proximateto a user equipment (UE). A UE can receive an event input from an eventinput source, e.g., as an event input via an N^(th) mode, e.g., 210-218,310-319, 410-418, etc. An event input source can be detectable orobservable by UE, wherein the detection and observation ca be by way ofthe event input via the N^(th) mode, for example the event input via anN^(th) mode can comprise password entry, an image of a user, audiocaptured from an area proximate to a user, EM radiation from an areaproximate to a user, facial recognition information, motion informationof the UE or of the area proximate to the UE, etc.

At 620, method 600 can comprise determining MEC congruence information(MECCI) based on MEC data from 610. A characteristic of an event inputreceived via a modality and can be extracted from MEC data and beemployed in an analysis of the significance of the characteristic inrelation to other characteristics from other event inputs received byother modalities that are contemporaneous with the event. As an example,where an event input is a video signal captured with a first videomodality, the characteristic be a state associated with the fingermovement of a user. This can be analyzed in relation to another eventinput, for example, an amount of jiggle captured by an accelerometermodality. This can be analyzed against other event inputs from an eventinput source. These characteristics can be associated with the sameinput device, e.g., a UE, smartphone, tablet, wearable device, vehicle,keyboard, touchscreen, microphone, internet of tings (IOT) enableddevice, etc. The analysis can then determine a level of congruencebetween the modality-event characteristics, e.g., how congruous is thefinger movement and jiggle level, how congruous is the finger movementand the other characteristics, how congruous is the jiggle level and theother characteristics, etc. From this example, where a true user istapping in a password on a touch screen mobile device, the fingermovement can be highly congruous with the jiggle levels over time, whichcan be employed to assert that the true user is entering the passwordbeing received, e.g., the finger movement appear to match the shaking ofthe mobile device in time and it can be more likely that it is an actualentry of the password data than if the finger movements did not ‘match’the jiggle of the device. As an example where the mobile is perfectlystill, it is less likely the user is actually tapping the touchscreen toenter the password data. Where a third, fourth, etc., MEC is alsoanalyzed for congruence, this can result in further refinement of thedetermined level of congruence between the characteristics and the entryof the password. For example, where the third MEC relates to theorientation of the device, where it is determined that the device isface down, and the fourth MEC relates to a front facing series ofimages, which are determined to be black (as would occur where a deviceis face down on a table), the level of congruence of the four MECs canbe low, namely that even though there is finger movement and jiggle,these are offset by device orientation and an image of a table top soclose to the device front that actually tap entry of a password isunlikely. The MEC congruence, e.g., MECCI, can be employed to initiate aresponse.

At 630, MECCI can be made available for access by other devices,systems, methods, etc. In an aspect, MECCI can be made available to a UEassociated with receiving event information to enable the UE to respondto the input event based on the levels of congruence determined, e.g.,different combinations of event inputs via different modalities, andMECs related thereto, can have different levels of congruence for eachcombination. By selecting a relevant combination of MECs, the associateddetermined level of congruence can be employed in determining acorresponding response.

At 640, method 600 can include, in response to the MECCI beingdetermined to satisfy a rule related to a profile value, initiating aresponse condition. At this point, method 600 can end. Rules andprofiles can also be employed in conjunction with the analysis of themodality-event characteristics. A rule can impart broadly applicableconventions to determining a level of congruence. As an example, a rulecan relate to determining similarity between waveforms, applyingthreshold values, defining normal rates of change in MEC values, etc.Profiles, in comparison to rules, can be employed to personalizecongruence determinations. A profile value can be comprised in one ormore profiles. A profile value, for example, can indicate weighting ofmodalities whereby some modalities can have a greater impact on MECCIthan other modalities, can indicate modalities that are to be ignored,modalities that are to always be used, etc., can designate differentrankings of modalities for different UEs, etc. As an example, a profilecan indicate that a video modality is to always be employed indetermining the level of congruence but that multiple video sourcesshould be selected when available. Moreover, profiles can comprisecharacteristics that can be associated with individual true users, forexample, images of the user, images of family members, the sound/voicesof a true user's office, user schedules, input devicetypes/models/brands/identities, etc. As such, where a plurality ofmodality-event characteristics are checked, there can beindividualization of the congruence determination based on the userprofile, as well as application of updateable general rules, that can beapplied to the congruence determination.

FIG. 7 illustrates a method 700 that facilitates determining acongruence of 1 to (N+1) event characteristics captured for an eventsource via 1 to (N+1) different modalities from a plurality of userequipments in accordance with aspects of the subject disclosure. At 710,method 700 can comprise receiving 1 to N event inputs for an event via 1to N modalities via a first UE. At 720, method 700 can comprisereceiving, by a second UE, an (N+1)^(th) event via a (N+1)^(th)modalities for the event. A UE can receive an event input from an eventinput source, e.g., as an event input via a 1^(st) modality, 2^(nd)modality, . . . , N^(th) modality, (N+1)^(th) modality, etc., e.g.,210-218, 310-319, 410-418, etc. An event input source can be detectableor observable by a first UE, second UE, etc., wherein the detection andobservation can be by way of the event input via the 1^(st) modality,2^(nd) modality, . . . , N^(th) modality, (N+1)^(th) modality, etc.

At 730, MEC data can be received from the first UE, second UE, etc.,based on the 1 to (N+1) event inputs received by the UEs at 710 and 720.MEC data can be related to an event input source that can be proximateto the first UE, the second UE, etc. The 1 to (N+1) event inputs caninherently or explicitly comprise MEC data, e.g., the MEC data cancomprise data related to a MEC of each modality-event associated withthe event input source. As disclosed herein, a MEC can be acharacteristic associated with an event and modality. As such, a MEC canbe extracted from nearly any information source contemporaneouslyassociated with an event via a given modality. Modalities can includevideo, images, audio, EM, motion, tilt, proximity, orientation,direction, pressure, temperature, capacitance, resistance, chemicalcomposition, etc., or even information itself, e.g., brand, model, make,manufacturer, source identification, encryption type, identifiedlanguage or dialect, etc. Moreover, different characteristics can becaptured for any given modality, for an example image, color saturation,facial recognition, fingerprint, iris pattern, skyline pattern, logo(s),weather, etc., for an example audio input, volume, frequency, spokenlanguage/dialect, sound pressure, the sound of a people in thebackground, the sound of a traffic in the background, etc. It will benoted that numerous other examples can be readily raised though theycannot all be explicitly stated herein for the sake of brevity andclarity.

At 740, method 700 can comprise determining MECCI based on MEC data from730. A characteristic of an event input received via a modality and canbe determined from MEC data and be employed in an analysis of thesignificance of the characteristic in relation to other characteristicsfrom other event inputs received by other modalities that arecontemporaneous with the event. These can be analyzed against otherevent inputs from an event input source. These characteristics can beassociated with the one or more input devices associated with an eventinput source, e.g., a UE, smartphone, tablet, wearable device, vehicle,keyboard, touchscreen, microphone, internet of things (TOT) enableddevice, etc., associated with an event input source. The analysis canthen determine a level of congruence between the modality-eventcharacteristics. The determined level of MEC congruence, e.g., comprisedin MECCI, can be employed to initiate a response.

At 750, method 700 can comprise enabling access to MECCI, e.g., MECCIcan be made available for access by other devices, systems, methods,etc. In an aspect, MECCI can be made available to one or more UEsassociated with receiving event information, e.g., the first UE at 710,the second UE at 720, etc., to enable a UE to respond to an input eventbased on the levels of congruence determined, e.g., differentcombinations of event inputs via different modalities, and MECs relatedthereto, can have different levels of congruence for each combination.By selecting a relevant combination of MECs, the associated determinedlevel of congruence can be employed in determining a correspondingresponse.

At 760, method 700 can include, in response to the MECCI beingdetermined to satisfy a rule related to a profile condition, initiatinga response condition. At this point, method 700 can end. Rules andprofiles can also be employed in conjunction with the analysis of themodality-event characteristics. A rule can impart broadly applicableconventions to determining a level of congruence. As an example, a rulecan relate to determining similarity between waveforms, applyingthreshold values, defining normal rates of change in MEC values, etc.Profiles, in comparison to rules, can be employed to personalizecongruence determinations. A profile value can be comprised in one ormore profiles. A profile value, for example, can indicate weighting ofmodalities whereby some modalities can have a greater impact on MECCIthan other modalities, can indicate modalities that are to be ignored,modalities that are to always be used, etc., can designate differentrankings of modalities for different UEs, etc. Moreover, profiles cancomprise characteristics that can be associated with individual trueusers, for example, images of the user, images of family members, thesound/voices of a true user's office, user schedules, input devicetypes/models/brands/identities, etc. As such, where a plurality ofmodality-event characteristics are checked, there can beindividualization of the congruence determination based on the userprofile, as well as application of updateable general rules, that can beapplied to the congruence determination.

FIG. 8 illustrates a method 800 that facilitates determining acongruence of event characteristics captured for an event via differentmodalities based on remotely stored rules in accordance with aspects ofthe subject disclosure. At 810, method 800 can comprise receiving MECdata. MEC data can comprise data related to a MEC. As disclosed herein,a MEC can be a characteristic associated with an event and modality. Assuch, a MEC can be extracted via a given modality from nearly anyinformation source contemporaneously associated with an event inputsource. Modalities can include video, images, audio, EM, motion, tilt,proximity, orientation, direction, pressure, temperature, capacitance,resistance, chemical composition, etc., or even information itself,e.g., brand, model, make, manufacturer, source identification,encryption type, identified language or dialect, etc. Moreover,different characteristics can be captured for any given modality, for anexample image, color saturation, facial recognition, fingerprint, irispattern, skyline pattern, logo(s), weather, etc., for an example audioinput, volume, frequency, spoken language/dialect, sound pressure, thesound of a people in the background, the sound of a traffic in thebackground, etc. It will be noted that numerous other examples can bereadily raised though they cannot all be explicitly stated herein forthe sake of brevity and clarity. MEC data can be related to an eventinput source that can be proximate to a UE. A UE can receive an eventinput from an event input source, e.g., as an event input via an N^(th)mode, e.g., 210-218, 310-319, 410-418, etc. An event input source can bedetectable or observable by UE, wherein the detection and observation cabe by way of the event input via the N^(th) mode.

At 820, Method 800 can comprise receiving a congruence rule from a datastore. The data store can store congruence rules including thecongruence rule received at 820. Rules and profiles can also be employedin conjunction with the analysis of the modality-event characteristics.A rule can impart broadly applicable conventions to determining a levelof congruence. As an example, a rule can relate to determiningsimilarity between waveforms, applying threshold values, defining normalrates of change in MEC values, etc. Profiles, in comparison to rules,can be employed to personalize congruence determinations. A profilevalue can be comprised in one or more profiles. A profile value, forexample, can indicate weighting of modalities whereby some modalitiescan have a greater or lesser impact on MECCI than other modalities, canindicate modalities that are to be ignored, modalities that are toalways be used, etc., can designate different rankings of modalities fordifferent UEs, etc. Moreover, profiles can comprise characteristics thatcan be associated with individual true users, for example, images of theuser, images of family members, the sound/voices of a true user'soffice, user schedules, input device types/models/brands/identities,etc. As such, where a plurality of modality-event characteristics arechecked, there can be individualization of the congruence determinationbased on the user profile, as well as application of updateable generalrules, that can be applied to the congruence determination.

Method 800, at 830, can comprise determining MECCI based on the MECdata, from 810, in response to determining that the MEC data satisfiesthe congruence rule from 820. As an example, where the congruence rulerelates to determining a level of similarity between waveform data,where the MEC data satisfies this rule, e.g., waveforms of the MEC dataare sufficiently similar (or dissimilar), then the MEC data can beemployed in determining the MECCI. In an aspect, determining MECCI basedon MEC data from 810 can include determine a characteristic of an eventinput received via a modality that can be extracted from MEC data and beemployed in an analysis of the significance of the characteristic inrelation to other characteristics from other event inputs received byother modalities that are contemporaneous with the event. These can beanalyzed in relation to other event inputs that satisfy a rule, per 820,and can be analyzed against other event inputs from an event inputsource. These characteristics can be associated with the same inputsource, e.g., a UE, smartphone, tablet, wearable device, vehicle,keyboard, touchscreen, microphone, internet of tings (IOT) enableddevice, etc., contemporaneously observing an event input source. Theanalysis can then determine a level of congruence between themodality-event characteristics. The MEC congruence, e.g., MECCI, can beemployed to initiate a response.

At 840, MECCI can be made available for access by other devices,systems, methods, etc. In an aspect, MECCI can be made available to a UEassociated with receiving event information to enable the UE to respondto the input event based on the levels of congruence determined, e.g.,different combinations of event inputs via different modalities, andMECs related thereto, can have different levels of congruence for eachcombination. By selecting a relevant combination of MECs, the associateddetermined level of congruence can be employed in determining acorresponding response.

At 850, method 800 can include, receiving profile information from thedata store, wherein the data store stores a profile associated with auser identity and includes a profile comprising the profile information.At 860, in response to the MECCI being determined to satisfy a rulerelated to the profile information from 850, method 800 can compriseinitiating a response condition. At this point, method 800 can end. Aspreviously disclosed, a profile can also be employed in conjunction withthe analysis of the modality-event characteristics. Profiles, incomparison to congruence rules, can be employed to personalizecongruence determinations. A profile value can be comprised in one ormore profiles. As such, where a plurality of modality-eventcharacteristics are checked, there can be individualization of thecongruence determination based on the user profile, as well asapplication of updateable general rules, that can be applied to thecongruence determination.

FIG. 9 is a schematic block diagram of a computing environment 900 withwhich the disclosed subject matter can interact. The system 900comprises one or more remote component(s) 910. The remote component(s)910 can be hardware and/or software (e.g., threads, processes, computingdevices). In some embodiments, remote component(s) 910 can compriseservers, personal servers, wireless telecommunication network devices,etc. As an example, remote component(s) 910 can be UEs 240, 340, 342,etc., MECAC 422, etc., a remote server, etc.

The system 900 also comprises one or more local component(s) 920. Thelocal component(s) 920 can be hardware and/or software (e.g., threads,processes, computing devices). In some embodiments, local component(s)920 can comprise, for example, MECAC 220, 320, 420, etc., UE 440, etc.

One possible communication between a remote component(s) 910 and a localcomponent(s) 920 can be in the form of a data packet adapted to betransmitted between two or more computer processes. Another possiblecommunication between a remote component(s) 910 and a local component(s)920 can be in the form of circuit-switched data adapted to betransmitted between two or more computer processes in radio time slots.The system 900 comprises a communication framework 940 that can beemployed to facilitate communications between the remote component(s)910 and the local component(s) 920, and can comprise an air interface,e.g., Uu interface of a UMTS network. Remote component(s) 910 can beoperably connected to one or more remote data store(s) 950, such as ahard drive, solid state drive, SIM card, device memory, etc., that canbe employed to store information on the remote component(s) 910 side ofcommunication framework 940. Similarly, local component(s) 920 can beoperably connected to one or more local data store(s) 930, that can beemployed to store information on the local component(s) 920 side ofcommunication framework 940.

In order to provide a context for the various aspects of the disclosedsubject matter, FIG. 10, and the following discussion, are intended toprovide a brief, general description of a suitable environment in whichthe various aspects of the disclosed subject matter can be implemented.While the subject matter has been described above in the general contextof computer-executable instructions of a computer program that runs on acomputer and/or computers, those skilled in the art will recognize thatthe disclosed subject matter also can be implemented in combination withother program modules. Generally, program modules comprise routines,programs, components, data structures, etc. that performs particulartasks and/or implement particular abstract data types.

In the subject specification, terms such as “store,” “storage,” “datastore,” “data storage,” “database,” and substantially any otherinformation storage component relevant to operation and functionality ofa component, refer to “memory components,” or entities embodied in a“memory” or components comprising the memory. It is noted that thememory components described herein can be either volatile memory ornonvolatile memory, or can comprise both volatile and nonvolatilememory, by way of illustration, and not limitation, volatile memory 1020(see below), non-volatile memory 1022 (see below), disk storage 1024(see below), and memory storage 1046 (see below). Further, nonvolatilememory can be included in read only memory, programmable read onlymemory, electrically programmable read only memory, electricallyerasable read only memory, or flash memory. Volatile memory can compriserandom access memory, which acts as external cache memory. By way ofillustration and not limitation, random access memory is available inmany forms such as synchronous random access memory, dynamic randomaccess memory, synchronous dynamic random access memory, double datarate synchronous dynamic random access memory, enhanced synchronousdynamic random access memory, Synchlink dynamic random access memory,and direct Rambus random access memory. Additionally, the disclosedmemory components of systems or methods herein are intended to comprise,without being limited to comprising, these and any other suitable typesof memory.

Moreover, it is noted that the disclosed subject matter can be practicedwith other computer system configurations, comprising single-processoror multiprocessor computer systems, mini-computing devices, mainframecomputers, as well as personal computers, hand-held computing devices(e.g., personal digital assistant, phone, watch, tablet computers,netbook computers, . . . ), microprocessor-based or programmableconsumer or industrial electronics, and the like. The illustratedaspects can also be practiced in distributed computing environmentswhere tasks are performed by remote processing devices that are linkedthrough a communications network; however, some if not all aspects ofthe subject disclosure can be practiced on stand-alone computers. In adistributed computing environment, program modules can be located inboth local and remote memory storage devices.

FIG. 10 illustrates a block diagram of a computing system 1000 operableto execute the disclosed systems and methods in accordance with anembodiment. Computer 1012, which can be, for example, MECAC 120, 220,320, 420, 422, 520, etc., UE 240, 340, 342, 440, etc., comprises aprocessing unit 1014, a system memory 1016, and a system bus 1018.System bus 1018 couples system components comprising, but not limitedto, system memory 1016 to processing unit 1014. Processing unit 1014 canbe any of various available processors. Dual microprocessors and othermultiprocessor architectures also can be employed as processing unit1014.

System bus 1018 can be any of several types of bus structure(s)comprising a memory bus or a memory controller, a peripheral bus or anexternal bus, and/or a local bus using any variety of available busarchitectures comprising, but not limited to, industrial standardarchitecture, micro-channel architecture, extended industrial standardarchitecture, intelligent drive electronics, video electronics standardsassociation local bus, peripheral component interconnect, card bus,universal serial bus, advanced graphics port, personal computer memorycard international association bus, Firewire (Institute of Electricaland Electronics Engineers 1194), and small computer systems interface.

System memory 1016 can comprise volatile memory 1020 and nonvolatilememory 1022. A basic input/output system, containing routines totransfer information between elements within computer 1012, such asduring start-up, can be stored in nonvolatile memory 1022. By way ofillustration, and not limitation, nonvolatile memory 1022 can compriseread only memory, programmable read only memory, electricallyprogrammable read only memory, electrically erasable read only memory,or flash memory. Volatile memory 1020 comprises read only memory, whichacts as external cache memory. By way of illustration and notlimitation, read only memory is available in many forms such assynchronous random access memory, dynamic read only memory, synchronousdynamic read only memory, double data rate synchronous dynamic read onlymemory, enhanced synchronous dynamic read only memory, Synchlink dynamicread only memory, Rambus direct read only memory, direct Rambus dynamicread only memory, and Rambus dynamic read only memory.

Computer 1012 can also comprise removable/non-removable,volatile/non-volatile computer storage media. FIG. 10 illustrates, forexample, disk storage 1024. Disk storage 1024 comprises, but is notlimited to, devices like a magnetic disk drive, floppy disk drive, tapedrive, flash memory card, or memory stick. In addition, disk storage1024 can comprise storage media separately or in combination with otherstorage media comprising, but not limited to, an optical disk drive suchas a compact disk read only memory device, compact disk recordabledrive, compact disk rewritable drive or a digital versatile disk readonly memory. To facilitate connection of the disk storage devices 1024to system bus 1018, a removable or non-removable interface is typicallyused, such as interface 1026.

Computing devices typically comprise a variety of media, which cancomprise computer-readable storage media or communications media, whichtwo terms are used herein differently from one another as follows.

Computer-readable storage media can be any available storage media thatcan be accessed by the computer and comprises both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structureddata, or unstructured data. Computer-readable storage media cancomprise, but are not limited to, read only memory, programmable readonly memory, electrically programmable read only memory, electricallyerasable read only memory, flash memory or other memory technology,compact disk read only memory, digital versatile disk or other opticaldisk storage, magnetic cassettes, magnetic tape, magnetic disk storageor other magnetic storage devices, or other tangible media which can beused to store desired information. In this regard, the term “tangible”herein as may be applied to storage, memory or computer-readable media,is to be understood to exclude only propagating intangible signals perse as a modifier and does not relinquish coverage of all standardstorage, memory or computer-readable media that are not only propagatingintangible signals per se. In an aspect, tangible media can comprisenon-transitory media wherein the term “non-transitory” herein as may beapplied to storage, memory or computer-readable media, is to beunderstood to exclude only propagating transitory signals per se as amodifier and does not relinquish coverage of all standard storage,memory or computer-readable media that are not only propagatingtransitory signals per se. Computer-readable storage media can beaccessed by one or more local or remote computing devices, e.g., viaaccess requests, queries or other data retrieval protocols, for avariety of operations with respect to the information stored by themedium. As such, for example, a computer-readable medium can compriseexecutable instructions stored thereon that, in response to execution,cause a system comprising a processor to perform operations, comprising:receiving trigger information a remote device, e.g., a UE, and inresponse, generating communication augmentation information that can beaccessed via an air interface or other wireless interface by one or moreservice interface components or other UEs to enable context sensitivecommunication augmentation.

Communications media typically embody computer-readable instructions,data structures, program modules or other structured or unstructureddata in a data signal such as a modulated data signal, e.g., a carrierwave or other transport mechanism, and comprises any informationdelivery or transport media. The term “modulated data signal” or signalsrefers to a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in one or moresignals. By way of example, and not limitation, communication mediacomprise wired media, such as a wired network or direct-wiredconnection, and wireless media such as acoustic, RF, infrared and otherwireless media.

It can be noted that FIG. 10 describes software that acts as anintermediary between users and computer resources described in suitableoperating environment 1000. Such software comprises an operating system1028. Operating system 1028, which can be stored on disk storage 1024,acts to control and allocate resources of computer system 1012. Systemapplications 1030 take advantage of the management of resources byoperating system 1028 through program modules 1032 and program data 1034stored either in system memory 1016 or on disk storage 1024. It is to benoted that the disclosed subject matter can be implemented with variousoperating systems or combinations of operating systems.

A user can enter commands or information into computer 1012 throughinput device(s) 1036. In some embodiments, a user interface can allowentry of user preference information, etc., and can be embodied in atouch sensitive display panel, a mouse input GUI, a command linecontrolled interface, etc., allowing a user to interact with computer1012. As an example, UE 240, 340, 342, 440, etc., can receive touch,motion, audio, visual, or other types of input. Input devices 1036comprise, but are not limited to, a pointing device such as a mouse,trackball, stylus, touch pad, keyboard, microphone, joystick, game pad,satellite dish, scanner, TV tuner card, digital camera, digital videocamera, web camera, cell phone, smartphone, tablet computer, etc. Theseand other input devices connect to processing unit 1014 through systembus 1018 by way of interface port(s) 1038. Interface port(s) 1038comprise, for example, a serial port, a parallel port, a game port, auniversal serial bus, an infrared port, a Bluetooth port, an IP port, ora logical port associated with a wireless service, etc. Output device(s)1040 use some of the same type of ports as input device(s) 1036.

Thus, for example, a universal serial busport can be used to provideinput to computer 1012 and to output information from computer 1012 toan output device 1040. Output adapter 1042 is provided to illustratethat there are some output devices 1040 like monitors, speakers, andprinters, among other output devices 1040, which use special adapters.Output adapters 1042 comprise, by way of illustration and notlimitation, video and sound cards that provide means of connectionbetween output device 1040 and system bus 1018. It should be noted thatother devices and/or systems of devices provide both input and outputcapabilities such as remote computer(s) 1044.

Computer 1012 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)1044. Remote computer(s) 1044 can be a personal computer, a server, arouter, a network PC, cloud storage, a cloud service, code executing ina cloud-computing environment, a workstation, a microprocessor basedappliance, a peer device, or other common network node and the like, andtypically comprises many or all of the elements described relative tocomputer 1012.

For purposes of brevity, only a memory storage device 1046 isillustrated with remote computer(s) 1044. Remote computer(s) 1044 islogically connected to computer 1012 through a network interface 1048and then physically connected by way of communication connection 1050.Network interface 1048 encompasses wire and/or wireless communicationnetworks such as local area networks and wide area networks. Local areanetwork technologies comprise fiber distributed data interface, copperdistributed data interface, Ethernet, Token Ring and the like. Wide areanetwork technologies comprise, but are not limited to, point-to-pointlinks, circuit-switching networks like integrated services digitalnetworks and variations thereon, packet switching networks, and digitalsubscriber lines. As noted below, wireless technologies may be used inaddition to or in place of the foregoing.

Communication connection(s) 1050 refer(s) to hardware/software employedto connect network interface 1048 to bus 1018. While communicationconnection 1050 is shown for illustrative clarity inside computer 1012,it can also be external to computer 1012. The hardware/software forconnection to network interface 1048 can comprise, for example, internaland external technologies such as modems, comprising regular telephonegrade modems, cable modems and digital subscriber line modems,integrated services digital network adapters, and Ethernet cards.

The above description of illustrated embodiments of the subjectdisclosure, comprising what is described in the Abstract, is notintended to be exhaustive or to limit the disclosed embodiments to theprecise forms disclosed. While specific embodiments and examples aredescribed herein for illustrative purposes, various modifications arepossible that are considered within the scope of such embodiments andexamples, as those skilled in the relevant art can recognize.

In this regard, while the disclosed subject matter has been described inconnection with various embodiments and corresponding Figures, whereapplicable, it is to be understood that other similar embodiments can beused or modifications and additions can be made to the describedembodiments for performing the same, similar, alternative, or substitutefunction of the disclosed subject matter without deviating therefrom.Therefore, the disclosed subject matter should not be limited to anysingle embodiment described herein, but rather should be construed inbreadth and scope in accordance with the appended claims below.

As it employed in the subject specification, the term “processor” canrefer to substantially any computing processing unit or devicecomprising, but not limited to comprising, single-core processors;single-processors with software multithread execution capability;multi-core processors; multi-core processors with software multithreadexecution capability; multi-core processors with hardware multithreadtechnology; parallel platforms; and parallel platforms with distributedshared memory. Additionally, a processor can refer to an integratedcircuit, an application specific integrated circuit, a digital signalprocessor, a field programmable gate array, a programmable logiccontroller, a complex programmable logic device, a discrete gate ortransistor logic, discrete hardware components, or any combinationthereof designed to perform the functions described herein. Processorscan exploit nano-scale architectures such as, but not limited to,molecular and quantum-dot based transistors, switches and gates, inorder to optimize space usage or enhance performance of user equipment.A processor may also be implemented as a combination of computingprocessing units.

As used in this application, the terms “component,” “system,”“platform,” “layer,” “selector,” “interface,” and the like are intendedto refer to a computer-related entity or an entity related to anoperational apparatus with one or more specific functionalities, whereinthe entity can be either hardware, a combination of hardware andsoftware, software, or software in execution. As an example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution, a program,and/or a computer. By way of illustration and not limitation, both anapplication running on a server and the server can be a component. Oneor more components may reside within a process and/or thread ofexecution and a component may be localized on one computer and/ordistributed between two or more computers. In addition, these componentscan execute from various computer readable media having various datastructures stored thereon. The components may communicate via localand/or remote processes such as in accordance with a signal having oneor more data packets (e.g., data from one component interacting withanother component in a local system, distributed system, and/or across anetwork such as the Internet with other systems via the signal). Asanother example, a component can be an apparatus with specificfunctionality provided by mechanical parts operated by electric orelectronic circuitry, which is operated by a software or firmwareapplication executed by a processor, wherein the processor can beinternal or external to the apparatus and executes at least a part ofthe software or firmware application. As yet another example, acomponent can be an apparatus that provides specific functionalitythrough electronic components without mechanical parts, the electroniccomponents can comprise a processor therein to execute software orfirmware that confers at least in part the functionality of theelectronic components.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. Moreover, articles “a” and “an” as used in thesubject specification and annexed drawings should generally be construedto mean “one or more” unless specified otherwise or clear from contextto be directed to a singular form.

Further, the term “include” is intended to be employed as an open orinclusive term, rather than a closed or exclusive term. The term“include” can be substituted with the term “comprising” and is to betreated with similar scope, unless otherwise explicitly used otherwise.As an example, “a basket of fruit including an apple” is to be treatedwith the same breadth of scope as, “a basket of fruit comprising anapple.”

Moreover, terms like “user equipment (UE),” “mobile station,” “mobile,”“subscriber station,” “subscriber equipment,” “access terminal,”“terminal,” “handset,” and similar terminology, refer to a wirelessdevice utilized by a subscriber or user of a wireless communicationservice to receive or convey data, control, voice, video, sound, gaming,or substantially any data-stream or signaling-stream. The foregoingterms are utilized interchangeably in the subject specification andrelated drawings. Likewise, the terms “access point,” “base station,”“Node B,” “evolved Node B,” “eNodeB,” “home Node B,” “home accesspoint,” and the like, are utilized interchangeably in the subjectapplication, and refer to a wireless network component or appliance thatserves and receives data, control, voice, video, sound, gaming, orsubstantially any data-stream or signaling-stream to and from a set ofsubscriber stations or provider enabled devices. Data and signalingstreams can comprise packetized or frame-based flows.

Additionally, the terms “core-network”, “core”, “core carrier network”,“carrier-side”, or similar terms can refer to components of atelecommunications network that typically provides some or all ofaggregation, authentication, call control and switching, charging,service invocation, or gateways. Aggregation can refer to the highestlevel of aggregation in a service provider network wherein the nextlevel in the hierarchy under the core nodes is the distribution networksand then the edge networks. UEs do not normally connect directly to thecore networks of a large service provider but can be routed to the coreby way of a switch or radio access network. Authentication can refer todeterminations regarding whether the user requesting a service from thetelecom network is authorized to do so within this network or not. Callcontrol and switching can refer determinations related to the futurecourse of a call stream across carrier equipment based on the callsignal processing. Charging can be related to the collation andprocessing of charging data generated by various network nodes. Twocommon types of charging mechanisms found in present day networks can beprepaid charging and postpaid charging. Service invocation can occurbased on some explicit action (e.g. call transfer) or implicitly (e.g.,call waiting). It is to be noted that service “execution” may or may notbe a core network functionality as third party network/nodes may takepart in actual service execution. A gateway can be present in the corenetwork to access other networks. Gateway functionality can be dependenton the type of the interface with another network.

Furthermore, the terms “user,” “subscriber,” “customer,” “consumer,”“prosumer,” “agent,” and the like are employed interchangeablythroughout the subject specification, unless context warrants particulardistinction(s) among the terms. It should be appreciated that such termscan refer to human entities or automated components (e.g., supportedthrough artificial intelligence, as through a capacity to makeinferences based on complex mathematical formalisms), that can providesimulated vision, sound recognition and so forth.

Aspects, features, or advantages of the subject matter can be exploitedin substantially any, or any, wired, broadcast, wirelesstelecommunication, radio technology or network, or combinations thereof.Non-limiting examples of such technologies or networks comprisebroadcast technologies (e.g., sub-Hertz, extremely low frequency, verylow frequency, low frequency, medium frequency, high frequency, veryhigh frequency, ultra-high frequency, super-high frequency, terahertzbroadcasts, etc.); Ethernet; X.25; powerline-type networking, e.g.,Powerline audio video Ethernet, etc.; femtocell technology; Wi-Fi;worldwide interoperability for microwave access; enhanced general packetradio service; third generation partnership project, long termevolution; third generation partnership project universal mobiletelecommunications system; third generation partnership project 2, ultramobile broadband; high speed packet access; high speed downlink packetaccess; high speed uplink packet access; enhanced data rates for globalsystem for mobile communication evolution radio access network;universal mobile telecommunications system terrestrial radio accessnetwork; or long term evolution advanced.

What has been described above includes examples of systems and methodsillustrative of the disclosed subject matter. It is, of course, notpossible to describe every combination of components or methods herein.One of ordinary skill in the art may recognize that many furthercombinations and permutations of the claimed subject matter arepossible. Furthermore, to the extent that the terms “includes,” “has,”“possesses,” and the like are used in the detailed description, claims,appendices and drawings such terms are intended to be inclusive in amanner similar to the term “comprising” as “comprising” is interpretedwhen employed as a transitional word in a claim.

What is claimed is:
 1. A device, comprising: a processor; and a memorythat stores executable instructions that, when executed by theprocessor, facilitate performance of operations, comprising: receivingmodality-event characteristic data comprising: first information relatedto a first modality-event characteristic of a first input purportedlycaptured with a first type of modality and associated with an inputevent source, and second information related to a second modality-eventcharacteristic of a second input captured with a second type of modalityand associated with the input event source; determining a first level ofcongruence between the first modality-event characteristic and thesecond modality-event characteristic of the modality-eventcharacteristic data, wherein the first level of congruence correspondsto a level of confidence that the first input purportedly captured withthe first type of modality has actually occurred via the first type ofmodality based on the second input being captured via the second type ofmodality being determined to be congruent with the first input beingcaptured via the first type of modality; and altering a validation ofthe first input based on the first level of congruence, wherein a use ofthe first input is predicated on the validation.
 2. The device of claim1, wherein the first modality-event characteristic is associated withvideo input data relating to the input event source and purportedlycaptured with a video type modality proximate to the input event source.3. The device of claim 2, wherein the first modality-eventcharacteristic is further associated with facial recognition input datarelating to the video input data.
 4. The device of claim 1, wherein thefirst modality-event characteristic is associated with audio input datarelating to the input event source and purportedly captured with anaudio type modality proximate to the input event source.
 5. The deviceof claim 4, wherein the first modality-event characteristic is furtherassociated with vocal analysis input data relating to the audio inputdata.
 6. The device of claim 1, wherein the first modality-eventcharacteristic is associated with electromagnetic radiation input datarelating to the input event source and purportedly captured with anelectromagnetic detection type modality proximate to the input eventsource.
 7. The device of claim 1, wherein the first modality-eventcharacteristic is associated with image input data relating to the inputevent source and purportedly captured with an imaging type modalityproximate to the input event source.
 8. The device of claim 1, whereinthe first modality-event characteristic is associated with motion inputdata relating to the input event source and purportedly captured with amotion detection type modality proximate to the input event source. 9.The device of claim 1, wherein the first modality-event characteristicis associated with biometric recognition data relating to the inputevent source and purportedly captured with a biometric detection typemodality proximate to the input event source.
 10. The device of claim 1,wherein the determining the first level of congruence betweenmodality-event characteristics of the modality-event characteristic datacomprises determining a rule relating to selecting the firstmodality-event characteristic has been satisfied.
 11. The device ofclaim 1, wherein the determining the first level of congruence betweenmodality-event characteristics of the modality-event characteristic datacomprises determining a rule relating to profile information of aprofile associated with a user identity has been satisfied.
 12. Thedevice of claim 1, wherein the first type of modality and the secondtype of modality are different types of modalities.
 13. The device ofclaim 1, wherein: the modality-event characteristic data furthercomprises third information related to a third modality-eventcharacteristic of a third input captured with a third type of modalityand associated with the input event source, and the determining thefirst level of congruence is based on the modality-event characteristicdata comprising the first information, second information, and thirdinformation related.
 14. The device of claim 1, wherein the initiatingthe response comprises initiating an alert.
 15. The device of claim 1,wherein the initiating the response comprises initiating a rejection ofan input associated with the first modality-event characteristic.
 16. Amethod, comprising: receiving, by a system comprising a processor, firstmodality-event characteristic data comprising first information relatedto a first modality-event characteristic of a first input that isindicated as being captured with a first type of modality and associatedwith an input event source; receiving, by the system, secondmodality-event characteristic data comprising second information relatedto a second modality-event characteristic of a second input capturedwith a second type of modality and associated with the input eventsource; determining, by the system, a level of congruence betweenmodality-event characteristics embodied in the first modality-eventcharacteristic data and second modality-event characteristic data,wherein the determining is based on determining that a first rulerelated to the congruence between the modality-event characteristics issatisfied, and wherein the level of congruence reflects that the firstinput has been verifiably captured with the first type of modality basedon the second input captured with the second type of modality beingdetermined to be congruent with the first input being captured by thefirst type of modality; and in response to determining that a secondrule relating to profile information of a profile associated with a useridentity is satisfied, enabling, by the system, use of the first inputby a device based on the level of congruence.
 17. The method of claim16, wherein the first type of modality and the second type of modalityare different.
 18. The method of claim 16, wherein the first type ofmodality and the second type of modality are a same type of modality.19. A non-transitory machine-readable storage medium, comprisingexecutable instructions that, when executed by a processor, facilitateperformance of operations, comprising: receiving modality-eventcharacteristic data comprising information related to modality-eventcharacteristics that correspond to inputs from an input event source,wherein the inputs are assumed to be captured via respective types ofmodalities and are associated with the input event source; determining alevel of congruence between the modality-event characteristics embodiedin the modality-event characteristic data based on determining that afirst rule related to congruence between modality-event characteristicsis satisfied, wherein the congruence is associated with the inputs trulybeing received via the assumed respective types of modalities; andinitiating use of the inputs from the input event source by a devicebased on the level of congruence.
 20. The non-transitorymachine-readable storage medium of claim 19, wherein the initiating theresponse comprises initiating a rejection of an input associated with amodality-event characteristic of the modality-event characteristics.